Dynamic Battery Research Articles

THE INFLUENCE OF PHYSICAL THERAPY ON INDICATORS OF LOCOMOTIVE SYNDROME IN ELDERLY PERSONS WITH OSTEOARTHRITIS OF THE KNEE AND OBESITY

Aim: to assess the effectiveness of the developed physical therapy program on locomotive syndrome parameters in elderly people with osteoarthritis of the knee and obesity.
 Material and methods. 93 elderly people were examined. The control group consisted of 31 people without osteoarthritis of the joints, with no obesity. The main group 1 consisted of 33 people who received rehabilitation according to the general principles of osteoarthritis correction. Representatives of the main group 2 (31 people) were engaged in a physical therapy program using functional training on the «Prosedos» platform, therapeutic exercises, Proprioceptive Neuromuscular Facilitation; massage, kinesiological taping, nutritional correction, patient education. The effectiveness of the program was evaluated according to the dynamics of the Short Physical Performance Battery, Functional Gait Assessment, Tinetti-test (Performance-Oriented Mobility Assessment), 25-question Geriatric Locomotive Function Scale.
 Results. Elderly patients with osteoarthritis of the knee and obesity were found to have impaired balance (Short Physical Performance Battery), altered gait stereotype (Functional Gait Assessment), risk of falling when performing activities of daily living (Tinetti-test) at the level of locomotive syndrome II degree (25- question Geriatric Locomotive Function Scale). The developed program of physical therapy in persons of the main group 2 revealed a statistically significantly better effect on the components of the locomotive syndrome compared to the initial examination in all studied parameters (p<0.05). Elderly patients who underwent rehabilitation according to the standard program for the correction of osteoarthritis achieved a statistically significant improvement compared to the initial state according to the studied parameters (p<0.05), but less pronounced compared to the tested program.
 Conclusions. Elderly patients with osteoarthritis of the knee joints and obesity need to develop physical therapy programs taking into account and correcting the specifics of each condition, the presence of static and dynamic balance disorders and the risk of falling, which will increase the overall effectiveness of rehabilitation measures.

Health-conscious predictive energy management strategy with hybrid speed predictor for plug-in hybrid electric vehicles: Investigating the impact of battery electro-thermal-aging models

Improving plug-in hybrid electric vehicles (PHEVs) fuel economy requires a proper energy management strategy (EMS). Efforts to enhance the energy-saving performance of predictive EMSs have concentrated on advanced speed prediction methods. However, the impact of battery models on the predictive EMS hasn't been investigated. This paper aims to fill the research gap by suggesting a model predictive control-based (MPC) EMS framework that uses a hybrid speed predictor. Firstly, six control-oriented battery electro-thermal-aging models with various terminal voltage and temperature simulation accuracies have been developed and verified. Secondly, it also examines the effects of different battery models on MPC-based EMS through quantitative analysis, including battery dynamics, calculation time, and resultant operating costs, which leads to the suggestions of model selection for the design of the EMS under low- and room-temperature driving scenarios. Finally, a novel hybrid speed prediction model is proposed, where the historical speed is decomposed into strongly periodic intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and backpropagation (BP) neural network is utilized to learn the feature parameter and mapping relationship of each IMF. In addition, a case study is conducted by applying the proposed speed prediction model in an MPC-based EMS method. The simulation results highlight that the proposed hybrid speed prediction model can achieve preferable speed prediction. The operating cost errors (compared with the MPC-based EMS with 100% speed prediction) are reduced to 0.4% and 0.98% at −20 °C and 25 °C driving scenarios, respectively.

Extreme learning machine model with honey badger algorithm based state-of-charge estimation of lithium-ion battery

Accurate state-of-charge (SOC) detection was still a challenging task to complete due to complex battery dynamics and constantly changing external conditions. The formula for SOC was difficult to determine since external parameters including voltage, current, temperature, and battery arrangement were complex. Also the methods for estimating SOC that were already in use were not always appropriate for the same car operating in various road and climatic conditions. In all situations, the conventional methodologies did not deliver an accurate estimation performance. Here, a unique optimization-based Extreme Learning Machine (ELM) was created to accurately determine a battery's SOC and enhance the operation and safety of battery systems. A lithium ion battery was first created, and data on its current, voltage, SOC, capacity, duration, and discharge rate were gathered to produce a real-time dataset at several temperatures, including 00,250 and 450. The dataset underwent additional pre-processing to standardize the values and enhance the accuracy of the data. To determine the precise state of the battery, these pre-data were loaded into the ELM model. However, the performance of ELM was significantly influenced by the length of training and the number of neurons in a hidden layer. An advanced Honey Badger Optimization Algorithm (HBA) was used to choose the appropriate hidden neurons and increase the estimation accuracy in order to overcome this problem. The proposed SOC estimation model provides 97% accuracy in the FUDS drive cycle and 99% accuracy in the US06 drive cycle. The proposed model provides a well performance for estimating SOC in lithium-ion battery at various temperature, also the proposed model was fit for real time implementation.

Dynamic battery swapping and rebalancing strategies for e-bike sharing systems

A new generation of the e-bike sharing system (EBSS) is emerging, where the e-bikes are dockless but need to be parked in designated zones defined by electric fences. The operation of the EBSS relies on the efficient swapping of batteries, in addition to e-bike rebalancing. The replaced power-deficient batteries can be charged in a central depot or street-side cabinets. This paper proposes an approach for modeling the system states of the EBSS containing e-bike stations and battery cabinet stations based on Markov chain dynamics considering both e-bike number and battery power level. Utilizing the prediction of e-bike inventory levels at e-bike stations and batteries’ status at e-bike stations/battery cabinets, a fast and adaptive one-step Markovian strategy is introduced, informing the operating staff of the next station to visit and detailed operations for battery swapping at e-bike/cabinet stations and rebalancing at e-bike stations. Besides, a rolling-horizon Markovian strategy is proposed to make a globally optimal plan of battery swapping and e-bike rebalancing by solving an integer nonlinear programming problem, which gives an approximate upper bound for integrated operations. By considering future demand and making optimal decisions, the proposed Markovian strategies can achieve a profit improvement of over 20% on average, compared to the current strategy used by the industry, as illustrated by the numerical simulations on a real-world EBSS.

Shared autonomous electric vehicle system design and optimization under dynamic battery degradation considering varying load conditions

Shared autonomous electric vehicles (SAEVs) that combine electric vehicles (EVs) with autonomous driving technologies and car-sharing services are expected to become a significant component of future transportation systems. However, the issue of battery degradation, which directly affects performance and efficiency, may limit their widespread adoption. To address this issue, this study proposes a comprehensive framework for SAEV system design and optimization in accordance with the dynamic nature of battery degradation under varying load conditions. The framework incorporates a semi-empirical method, which is a predictive approach capable of estimating capacity changes based on factors such as temperature, C-rate, cycle number, and depth of discharge (DOD) for each cycle. The analysis confirmed that battery degradation significantly affected the actual simulation and system design outcomes, which further highlights the significance of dynamic input conditions. Notably, the DOD limit and charging power were identified as the key design variables, warranting separate scenario analyses because of their critical impact on battery degradation. When battery degradation was considered and both the DOD and charging power were used as design variables, the total cost was reduced by 51.3% ($776,708) compared to the case where battery degradation was not considered. Furthermore, the necessary adaptations in the SAEV design and operation under different scenarios were determined. For this, additional parametric studies were conducted by altering various operating conditions, such as the waiting time constraints, fleet cost, and battery degradation rate. The accurate prediction of battery degradation using the current model provides valuable insights and guidance for SAEV design and operation, thereby ensuring longevity and optimized performance.

Revealing hidden predicaments to lithium-ion battery dynamics for electric vertical take-off and landing aircraft

Revealing hidden predicaments to lithium-ion battery dynamics for electric vertical take-off and landing aircraft

Lithium-ion battery state-of-charge estimation based on long-short-term memory neural network and square-root cubature Kalman filter

Due to the widespread use of Li-ion batteries in electric vehicles, battery management systems for monitoring the status and ensuring the safe operation of Li-ion batteries have been extensively studied. Online monitoring of the state of charge (SOC) is crucial for lithium-ion batteries, but achieving precise SOC estimation is a difficult task due to battery dynamics and the influence of factors such as current, temperature, and operating conditions on SOC variability. This paper introduces a novel approach that combines a Long Short-Term Memory (LSTM) network with a square-root cubature Kalman filter (SRCKF) to address this challenge. To tackle the issue at hand, the proposed methodology employs a two-step approach. Initially, the LSTM network is utilized to capture the complex relationship between the state of charge (SOC) and the measured variables, including current, voltage, and temperature. Subsequently, the output of the LSTM network is subjected to smoothing using the SRCKF technique, leading to precise and consistent SOC estimation. A notable advantage of this method is its ability to simplify the arduous task of parameter tuning for the LSTM network, eliminating the need for constructing a battery model. The experimental results demonstrate that the maximum error in estimating the state of charge (SOC) using this particular method is constrained within the 5% threshold. Compared with using a separate LSTM method at different temperatures, by using the combination method, the root mean square error and maximum error of SOC estimation are greatly reduced.

Privacy-Preserving Operation Management of Battery Swapping and Charging System With Dual-Based Benders Decomposition

The battery swapping and charging system (BSCS) is an emerging and promising infrastructure to provide the energy refueling service for EVs. However, each operator (i.e., battery swapping module (BSM) operator and battery charging module (BCM) operator) in BSCS has privacy-preserving requirement and individual interest, which brings challenge to the operation management of BSCS. To this end, this paper investigates the privacy-preserving operation management problem (OMP) of BSCS from a social perspective, in which a comprehensive battery-swapping-charging process is modeled. Specifically, we first model the OMP as a constrained mixed-integer programming (MIP), where the evolutionary of the dynamic battery inventory and the SoC of charging batteries are considered simultaneously. Due to the privacy-preserving requirements of different operators and the strong coupling between continuous and binary decision variables, a novel dual-based Benders decomposition (DBD) algorithm is then developed, which has the following two merits: 1) Each BCM and BSM operator can independently solve its own charging and swapping scheduling problem, thus preserving the privacy of individual operator; 2) The combination of dual decomposition and Benders decomposition techniques facilitates a parallel implementation, thus improving the scheduling efficiency and saving the computational time. Finally, simulation results are provided to validate the effectiveness and scalability of the proposed algorithm.

A voltage dynamics model of lithium-ion battery for state-of-charge estimation by proportional-integral observer

Observer-based algorithms for the state-of-charge estimation require setting up a dynamics model in battery management system of lithium-ion battery, and it is more important for the model to characterize the battery dynamics as much as possible. This is particularly challenging for formulating several characteristics into one battery dynamics model, for which the electrochemical reaction principle consists of some different disciplinary theories. In this article, a voltage dynamics model is developed for the design of a proportional-integral observer, which fuses the battery voltage characteristics, the current integral principle and the equivalent circuit model into a set of differential equations. Simultaneously, the model parameters are also updated in real time. Here we discuss a series of studies on the model-based algorithms that, collectively, develop a proportional-integral observer of how the state-of-charge is estimated by the use of the voltage dynamics model. Then, the proportional-integral observer algorithm is downloaded into a battery management system, which is deployed in a battery electric vehicle for the validation. Among the proportional-integral observer, the current integral principle and the extended Kalman filter, some comparative experiments are done with a same test cycle. Based on the experimental results and the statistical analyses, the proportional-integral observer by the voltage dynamics model is a good candidate for the state-of-charge estimation of lithium-ion battery in a real-world vehicle.

Use of Physics-Based Models for Different Applications of Electrochemical Energy Storage and Conversion Devices

This talk will present the application of physics-based models applied to two different electrochemical storage and conversion systems, viz. Li-ion batteries and Proton Exchange Membrane Fuel Cells (PEMFCs). Battery energy storage system (BESS) is becoming a crucial part of standalone renewable hybrid power systems due to the intermittent nature of power generation and for aiding in various operations like frequency regulation, voltage support, and peak shaving. The lithium-ion battery is a promising choice for BESS due to its high energy density, power density, operating voltage, negligible self-discharge, suitable operating temperature range, modularity, and reliability. A standalone renewable hybrid power system framework with lithium-ion battery energy storage is developed to investigate its performance using physics-based battery models. Power management and control strategies are developed for standalone renewable hybrid power systems. These control strategies can track the MPP of the solar cells and wind turbines while avoiding overcharging the battery and guaranteeing 0% dumping power under different ambient and working conditions. The battery is exposed to harsh ambient conditions in the real world, resulting in the dynamic and continuous capacity fade. A thermal management and control strategy is developed to analyze the effect of temperature on lithium-ion batteries' performance and degradation without using external cooling systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of renewable hybrid power systems. The impact of BESS size variation on degradation and the cost of energy generation is analyzed. The developed renewable hybrid power system framework is independent of location and system size and can be extended to incorporate other renewable energy generation sources and energy storage systems.The second part of this talk will focus on using physics-based models to understand the water dynamics and degradation mechanisms inside PEMFC, which is necessary for water management, long-life operation, and cost reduction. PEMFCs have emerged as an alternative green energy technology in various applications such as automobiles, portable and stationary applications, and auxiliary power supplies for space exploration missions. The cost and durability targets remain unmet largely, which restrains the commercialization of the PEMFCs. The major difficulty in modeling lies in the wide scale of dimensions from the millimeter level (flow channel) to the micron level (catalyst layer (CL) thickness). The multi-scale physics-based model enables the life cycle analysis of PEMFC under various operating conditions. The mass transport limitation is the major hurdle in performance enhancement for higher current density operations. It arises from the liquid water generated during the electrochemical reactions, which hinders the reaction sites of CL and oxygen transport reaction sites in the gas diffusion layer, resulting in performance deterioration. Many experimental studies reported the two-phase flow visualization techniques in PEMFCs, viz. neutron imaging, optical visualization, and X-ray radiography. However, the difficulties in conducting experiments for flow visualization and related parameters such as liquid volume fraction have encouraged researchers to adopt numerical simulations that are less expensive and computationally efficient. Understanding liquid water movement inside the cell at the micro-scale help to improve the PEMFC performance at the stack level. Here, we develop a comprehensive 3-D, multiphase, non-isothermal, steady-state physics-based model of the PEMFC that can closely approximate the liquid water flooding and membrane drying. The findings from this work can be used to design an efficient flow configuration and to study membrane deformation due to the shrinking-swelling under humidification and thermal cycling. The ultimate goal of the multi-scale model is to enhance the PEMFC stack output with less degradation.

Polypyrrole-Doped NH4 V3 O8 with Oxygen Vacancies as High-Performance Cathode Material for Aqueous Zinc-Ion Batteries.

The inherent slow diffusion dynamics of aqueous zinc-ion batteries (AZIBs) act as a significant hindrance to their universal utilization as energy storage systems, largely attributed to the scarcity of superior cathode materials. In this study, a novel method that amalgamates oxygen defect engineering and polymer intercalation, guided by theoretical computations, to confront this challenge, is introduced. Thisapproach begins with density functional theory calculations, demonstrating that the shielding effect rendered by polypyrrole (PPy) between NH4 V3 O8 (NVO) layers, along with the cooperative influence of oxygen defects (Od ), optimizes the kinetic transport of Zn2+ . Leveraging these theoretical outcomes, a two-step hydrothermal synthesis procedure is devised to fabricate PPy-intercalated NVO embedded with Od (NVO-Od @PPy). Theempirical findings corroborate the theoretical predictions, showcasing that the NVO-Od @PPy//Zn system manifests exceptional cycling stability. Specifically, the NVO-Od @PPy electrode delivers an optimal reversible capacity, yielding 421 mAh g-1 at a current density of 0.1 A g-1 . Remarkably, even at an elevated current density of 10 A g-1 , it sustains a capacity of 175.7 mAh g-1 , while maintaining a capacity retention of 99% over 1000 cycles. This research provides pivotal insights for the engineering of high-performing cathode materials for AZIBs, paving the way for their future advancements.

Abstraction and simulation of EV battery systems—resilience engineering by biological transformation

While the demand for electric vehicles (EVs) is continuously growing, safety issues still remain, specifically related to fire hazards. This research aims to improve the resilience of battery systems in EVs by transferring concepts found in biology to a bioinspired battery system. Due to the complexity of modern battery systems, the biological concepts cannot be applied directly. A simplified simulation battery system for EVs is modelled, which contains the essential battery components necessary to understand both, software and battery dynamics. This is used as a baseline model to study the effects of typical heat-related disturbances. Subsequently, this simulation model is modified to demonstrate the transfer of biological concepts underlying specifically the hypersensitization and vasospasm mechanisms related to wound healing, and to test the effects of disturbances and alterations comparable to damages caused by vehicle accidents. As a battery system’s mass and volume should not be increased by additional hardware, the biological concepts target the interaction within, and the composition of, the system, while leaving single components relatively unchanged. It is found that small bioinspired alterations to the battery system can have significant impacts on their vulnerability to common hazards.

Hybrid Cathode Lithium Battery Discharge Simulation for Implantable Cardioverter Defibrillators Using a Coupled Electro-Thermal Dynamic Model.

This paper explores the effect of the load imposed by implantable cardioverter defibrillators (ICDs) on their lithium battery power sources longevity using a simulation approach that incorporates a coupled electro-thermal dynamic model. ICDs are one of the effective treatments available to significantly improve survival of patients with fatal arrhythmia (abnormal heart rhythm) disorders. Using a lithium battery power source, this life-saving device sends electrical shocks or pulses to regulate the heartbeat. The service life and reliability of an ICD is primarily expressed by its battery's lifespan and performance. In this paper we investigate the terminal voltage, depth of discharge and temperature dynamics of the implantable lithium battery with a combined cathode material, namely silver vanadium oxide and carbon-monofluoride (Li/SVO-CFx). Modeling the implantable batteries characteristics is a well-established topic in literature; however, to the best of the author's knowledge, the impact of the high-energy shocks (defibrillation) and low-energy device power supply (housekeeping) on the ICD's battery operation is relatively less-explored. Our analysis reveals that the battery terminal voltage is primarily influenced by the continuous low-level housekeeping discharge current within the range of micro amps, rather than the intermittent high-level demand of defibrillation current within the range of several amperes. A Monte Carlo simulation and model prediction comparison with real-time experimental data from literature were used to assess the accuracy and applicability of the model. The results can be used to improve the device battery design, control and operation, thus extending the service life in patients and reducing the need for invasive replacement surgery.

Enhancing hybrid energy storage systems with advanced low-pass filtration and frequency decoupling for optimal power allocation and reliability of cluster of DC-microgrids

This study introduces an innovative power-split approach for hybrid energy storage systems (HESS) and diesel generators, utilizing frequency decoupling and a combination of classical and advanced low-pass-filtration techniques. The HESS dual-loop structure incorporates Super-Twisting-Sliding-Mode-Control, known for its remarkable robustness against uncertainties and external disturbances, effectively suppressing chattering. Among the low-pass-filtration techniques, Advanced Low-Pass-Filtering (ALPF) surpasses Classical Low-Pass-Filtering (CLPF), offering enhanced control statistics. The regulation of supercapacitor setpoint current based on the bank voltage ratio and control inaccuracy of battery current enables the ALPF to address dynamic battery issues. Under three waveforms, thorough simulations show superior load power allocation performance. Smoother waveforms boost supply voltage by 36% and power flow by 0.2%. Root-Mean-Squared-Error and Mean-Absolute-Error evaluations show exceptionally low average errors of 0.72% for Bus voltage, 0.16 A for battery current, and 3 A for supercapacitor current. The system also achieves 99.8% supply efficiency with a load setpoint deviation of less than 1%. ALPF improves supercapacitor performance by 8.4–11% while adjusting for uncompensated battery power. ALPF improves voltage regulation, power losses, load setpoint convergence, power peaks, and recovery. Ultimately, the proposed low-pass-filtration technique significantly enhances system dependability, relieving the burden on HESS while achieving superior loading efficiency.

A non-linear model predictive control strategy to minimise mechanical degradation effects of lithium-ion battery

Mechanical stresses are not only developed inside the active material of the composite electrode, but also in the solid electrolyte interface layer (SEI) due to the graphite expansion. Due to diffusion induced stress (DIS) phenomena, stresses in the negative electrode are generated during charging and discharging cycles. This article presents the proposed optimal charging profile explicitly incorporating the effects of mechanical degradation. Non-linear model predictive control approach along with Gauss pseudo-spectral method is used to optimise charging trajectories. It is assumed that active material is not the weakest material but system is modelled based on SEI break and repair effect. The dynamics of battery is represented by a single particle model. The simulated results are compared with benchmark constant current constant voltage (CCCV) strategy. Furthermore, proposed methodology is compared with optimal CCCV using cycle life ageing experimental scenario. It is estimated that stress amplitude decreased by 7% and nominal capacity increased by 11% using the proposed optimal charging profile.

Modeling and Validation of a Lithium-Ion Battery Pack

Abstract Batteries are part of an extremely nonlinear electrochemical system, and their performance is determined by various key factors such as law of thermodynamics, electrode kinetics, ion transport, and diffusion. To develop an accurate model of a battery, it is important to take into consideration both the internal and external conditions that would determine both the battery dynamics and its performance. Electrochemical, mathematical, and electrical equivalent circuit models are three common ways to model the electrochemical battery system. However, using these models involves certain limitations such as increased computation, empirical equations, and components (both active and passive). A simple electrical battery model with a voltage source and a resistor is insufficient to attain the desired level of accuracy in the model. To overcome these limitations, this study presents an electrothermal model that has been designed by adding “n” number of resistance capacitance (RC) pairs connected parallelly to ensure both dynamics and accuracy. An electrothermal model of a lithium-ion cell with a one-RC model and two-RC model has been developed, and a performance comparison has also been made to check the voltage accuracy of all the models. The electrical and thermal parameters (Em (state of charge [SOC], T), RO (SOC, T), R1 (SOC, T), R2 (SOC, T), C1 (SOC, T), C2 (SOC, T)) required for an RC model have been extracted through the pulse charge-discharge test. A battery pack of 55 V, 50 Ah using the RC model based on the Indian driving cycle condition has also been developed. Pulse charge-discharge and constant current-constant voltage profiles have been applied to the developed battery pack model to simulate and validate the accuracy of voltage in a real-time scenario.

Advanced cycling ageing-driven circular economy with E-mobility-based energy sharing and lithium battery cascade utilisation in a district community

Electrochemical storage technologies are essential for transformation towards electrification in both building and transportation sectors. The high cost, intensified carbon density and performance ageing of electrochemical batteries prevent its sustainable applications. Many studies have explored the battery circular economy based on reusing retired EV batteries with economic and environmental competitiveness. However, few studies combine battery circular economy with E-mobility-based interactive renewable energy sharing network for sustainability, together with comprehensive considerations on dynamic battery performance and degradation. In this study, a building-vehicle energy interaction model and a battery circular economy model were developed to simulate the process of reusing retired EV batteries. Considering the inconsistency in the carbon intensity of battery operation, manufacturing and recycling phases, a lifecycle battery carbon intensity criterion is proposed to quantify the lifetime carbon of batteries, and assess the decarbonisation capability of different renewable sources, battery reusing and V2B/B2V interaction, respectively. Results show that reusing retired batteries from EVs to buildings can improve the demand coverage ratio (DCR) from 0.480 to 0.504 and the renewable penetration ratio (RPR) from 0.502 to 0.531, respectively. Furthermore, the battery carbon intensity cannot be offset unless both renewables and reused batteries are integrated. The case study demonstrates the decrease of battery carbon intensity from 1284.57 kg CO2,e/kWh to −720.79 kg CO2,e/kWh with renewable and reused batteries. Research results can provide innovative battery circular economy frameworks for electrification transformation, decarbonisation and sustainable development.

A quantitative method for early-stage detection of the internal-short-circuit in Lithium-ion battery pack under float-charging conditions

Detecting the internal short circuit (ISC) of Lithium-ion batteries is critically important for preventing thermal runaway. Conventional approaches mainly focus on ISC detection for dynamic load profiles, while the commonly seen float-charging scenarios with a high risk of ISC are rarely considered. Technical challenges arise from not only the lack of models describing battery dynamics at fully charged conditions but also the computational burden caused by the high number of series-connected cells in a battery pack. To address these issues, we here propose a simple and accurate method to quantitatively identify the leakage current of the battery with ISC, by checking the behaviors of the battery equalization system. Battery-in-the-loop experiments are carried out to verify the proposed method under a wide range of the short circuit resistances (∼20 to ∼500Ω), using both LiFePO4 and Li(NiCoMn)0.33O2 batteries with different aging degrees. The typical errors of the identified leakage currents can be well-bounded within ±1 mA, including the cases of using both passive and active balancing. The proposed method is completely model-free and, therefore, can be transplanted into low-cost embedded systems to support broader applications.

Nonlinear identifiability analysis of Multiphase Porous Electrode Theory-based battery models: A Lithium Iron Phosphate case study

Porous electrode theory (PET) is widely used to model battery dynamics by describing electrochemical kinetics and transport in solid particles and electrolyte. Standard PET models rely on black-box descriptions of the thermodynamics of active materials, typically obtained by fitting an open-circuit potential which does not allow for a consistent description of phase-separating materials. Multiphase PET (MPET) was recently developed to describe batteries using white- or gray-box descriptions of the thermodynamics with additional parameters that need to be estimated from experimental data. This work analyzes the identifiability of parameters in the MPET model, including the standard kinetics and diffusion parameters, as well as MPET-specific parameters for the free energy of active materials. Based on synthetic discharge data, both linearized and nonlinear identifiability analyses are performed for an MPET model of a commercial Lithium Iron Phosphate/Graphite battery, which identify which model parameters are not identifiable and which are identifiable only with large uncertainty. The identifiable parameters control phase separation, reaction kinetics, and electrolyte transport, but not solid diffusion, consistent with rate limitation by intercalation reactions at low rates and by electrolyte diffusion at high rates. The article also proposes approaches for reducing parameter identifiability issues.

THE INFLUENCE OF PHYSICAL THERAPY ON INDICATORS OF THE LOCOMOTIVE SYNDROME IN ELDERLY PERSONS WITH THE CONSEQUENCES OF TOTAL KNEE ARTHROPLASTY

Objective: to evaluate the effectiveness of the developed physical therapy program on the parameters of the locomotive syndrome in elderly people with the motor consequences of total knee arthroplasty against the background of the geriatric syndrome of sarcopenia, in the remote period after the operation.
 Methods. 80 elderly people were examined. The control group consisted of 24 people without joint endoprostheses, with no signs of sarcopenia. The comparison group consisted of individuals with knee arthroplasty and sarcopenia with low rehabilitation compliance with regard to active rehabilitation intervention. Representatives of the main group with knee endoprosthesis and sarcopenia practiced according to a developed program of physical therapy, created taking into account the specifics of each comorbid condition. The program lasted 6 months; contained the following elements: therapeutic exercises, functional training, massage, kinesiotaping; dietary recommendations; patient education. Its purpose was: correction of the phenomena of motor stereotype violation as a consequence of total knee arthroplasty, in particular, phenomena of the locomotive syndrome, leveling of the phenomena of sarcopenia both at the level of its motor manifestations and at the level of etiopathogenesis of this geriatric condition; facilitation of activities of daily life, improvement of psycho-emotional state, and – as a result – improvement of quality of life. In the process of physical therapy, individual short- and long-term goals defined in the SMART format were consistently achieved within the framework of the patient-centered rehabilitation model. The effectiveness of the program was evaluated according to the dynamics of The Short Physical Performance Battery, Functional Gait Assessment, Fall efficacy scale, 25-question Geriatric Locomotive Function Scale.
 Results. In elderly patients with knee arthroplasty and sarcopenia, signs of locomotor syndrome were found in the form of impaired balance when performing simple movements (according to The Short Physical Performance Battery), violation of the normal gait stereotype and the resulting risk of falling (according to the Functional Gait Assessment), which led to awareness of the fear of falling (according to the Fall efficacy scale). The hall level of movement disorders corresponded to locomotive syndrome of the II degree (according to the 25-question Geriatric Locomotive Function Scale). The developed physical therapy program revealed a statistical improvement in the condition of patients due to the effect on the components of the locomotive syndrome due to the improvement of balance and gait parameters, reduction of the risk of falling and fear of falling compared to the initial indicators for all the studied parameters (р<0.05); however, the indicators of the control group were not reached (p>0.05). Low rehabilitation compliance in patients with knee arthroplasty and sarcopenia, despite awareness of the risks of this comorbidity, was associated with a lack of improvement over a similar follow-up period.
 Conclusions. Elderly patients with the consequences of total knee arthroplasty and sarcopenia require the development of physical therapy programs taking into account and correcting the specifics of each condition, the presence of physical status disorders and the risk of falling, which will increase the overall effectiveness of rehabilitation measures.